Utilizing machine learning to generate augmented reality vehicle information for a scale model of a vehicle

ABSTRACT

A device receives an image including image data of a scale model of a vehicle, and processes the image data, with a model, to identify a make, a model, and a year represented by the scale model. The device determines augmented reality (AR) vehicle information based on the make, the model, and the year represented by the scale model of the vehicle, and provides the AR vehicle information to enable a user device to associate the AR vehicle information with the image of the scale model of the vehicle. The device receives an input associated with the AR vehicle information, and determines updated AR vehicle information based on the input associated with the AR vehicle information. The device provides the updated AR vehicle information to enable the user device to associate the updated augmented reality vehicle information with the image of the scale model of the vehicle.

RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.17/448,936, filed Sep. 27, 2021, which is a continuation of Ser. No.16/740,854, filed Jan. 13, 2020 (now U.S. Pat. No. 11,145,131), which isa continuation of U.S. patent application Ser. No. 16/277,843, filedFeb. 15, 2019 (now U.S. Pat. No. 10,535,201), the contents of which areincorporated herein by reference in their entireties.

BACKGROUND

Vehicle dealerships are one of the few remaining businesses that haveyet to be replaced by e-commerce websites. This is due to customerswanting more from a vehicle-buying experience than what a simple clickof a button can provide. Customers want education about the vehicles,test drives of the vehicles, all-around service for the vehicles, and/orthe like. In order to sell vehicles, vehicle dealerships typicallymaintain large lots of new and/or used vehicles for customers to viewand/or test drive.

SUMMARY

According to some implementations, a method may include receiving animage of a scale model of a vehicle, wherein the image may betransmitted by a user device, and processing, the image, with a model,to determine vehicle information associated with the scale model of thevehicle, wherein the vehicle information includes at least one ofinformation identifying a year of the vehicle, information identifying amake of the vehicle, or information identifying a model of the vehicle.The method may include providing the vehicle information to the userdevice, and receiving, from the user device, an input associated withthe vehicle information. The method may include determining augmentedreality vehicle information based on the input associated with thevehicle information, wherein the augmented reality vehicle informationmay include augmented reality information to be associated with theimage of the scale model of the vehicle, and providing the augmentedreality vehicle information to the user device to enable the user deviceto associate the augmented reality vehicle information with the image ofthe scale model of the vehicle.

According to some implementations, a device may include one or morememories, and one or more processors, communicatively coupled to the oneor more memories, configured to receive an image including image data ofa scale model of a vehicle, wherein the image may be captured by a userdevice. The one or more processors may process the image data, with amodel, to identify a make, a model, and a year represented by the scalemodel of the vehicle, and may determine augmented reality vehicleinformation of a real world version of the vehicle based on the make,the model, and the year represented by the scale model of the vehicle,wherein the augmented reality vehicle information may include augmentedreality information to be associated with the image of the scale modelof the vehicle. The one or more processors may provide the augmentedreality vehicle information to the user device to enable the user deviceto associate the augmented reality vehicle information with the image ofthe scale model of the vehicle, and may receive, from the user device,an input associated with the augmented reality vehicle information. Theone or more processors may determine updated augmented reality vehicleinformation based on the input associated with the augmented realityvehicle information, and may provide the updated augmented realityvehicle information to the user device to enable the user device toassociate the updated augmented reality vehicle information with theimage of the scale model of the vehicle.

According to some implementations, a non-transitory computer-readablemedium may store instructions that include one or more instructionsthat, when executed by one or more processors of a user device, causethe one or more processors to receive an image of a scale model of avehicle, and process the image, with a model, to identify a vehicle typeassociated with the scale model of the vehicle. The one or moreinstructions may cause the one or more processors to determine augmentedreality vehicle information based on the vehicle type associated withthe scale model of the vehicle, wherein the augmented reality vehicleinformation may include augmented reality information to be associatedwith the image of the scale model of the vehicle. The one or moreinstructions may cause the one or more processors to provide, fordisplay, the augmented reality vehicle information in association withthe image of the scale model of the vehicle, and receive an inputassociated with the augmented reality vehicle information. The one ormore instructions may cause the one or more processors to determineupdated augmented reality vehicle information based on the inputassociated with the augmented reality vehicle information, and provide,for display, the updated augmented reality vehicle information inassociation with the image of the scale model of the vehicle.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-1H are diagrams of an example implementation described herein.

FIG. 2 is a diagram of an example environment in which systems and/ormethods, described herein, may be implemented.

FIG. 3 is a diagram of example components of one or more devices of FIG.2 .

FIGS. 4-6 are flow charts of example processes for utilizing machinelearning to generate augmented reality vehicle information for a scalemodel of a vehicle.

DETAILED DESCRIPTION

The following detailed description of example implementations refers tothe accompanying drawings. The same reference numbers in differentdrawings may identify the same or similar elements.

Maintaining large lots of new and/or used vehicles for customers to viewand/or test drive at vehicle dealerships requires buying and/or leasingproperties for the lots. Such properties are cumbersome to maintain andexpensive to buy and/or lease for vehicle dealerships. Furthermore, alarge inventory of new and/or used vehicles is also cumbersome tomaintain and expensive for vehicle dealerships.

Some implementations described herein provide an augmented reality (AR)delivery platform that utilizes machine learning to generate augmentedreality vehicle information for a scale model of a vehicle. For example,the AR delivery platform may receive an image of a scale model of avehicle, wherein the image may be transmitted by a user device, and mayprocess the image, with a machine learning model, to determine vehicleinformation associated with the scale model of the vehicle, wherein thevehicle information may include at least one of the image of the scalemodel of the vehicle, information identifying a year of the vehicle,information identifying a make of the vehicle, or informationidentifying a model of the vehicle. The AR delivery platform may providethe vehicle information to the user device, and may receive, from theuser device, an input associated with the vehicle information. The ARdelivery platform may determine AR vehicle information based on theinput associated with the vehicle information, wherein the AR vehicleinformation may include AR information to be associated with the imageof the scale model of the vehicle, and may provide the AR vehicleinformation to the user device to enable the user device to associatethe augmented reality vehicle information with the image of the scalemodel of the vehicle.

In this way, a dealership or showroom need only display scale models ofvehicles that represent real world vehicles in an inventory, for order,and/or the like. A customer may walk around the scale model showroom andbrowse vehicles in AR (or virtual reality) as if the customer is on avehicle lot or in a showroom. Thus, the AR delivery platform provides apractical, inexpensive, and convenient way to display inventories of newand/or used vehicles to customers. The AR delivery platform may provideaugmented reality information that enables a vehicle dealership todisplay entire inventories of new and/or used vehicles to customers,without requiring the vehicle dealership to purchase and/or leaseproperties for the new and/or used vehicles. By displaying entireinventories of new and/or used vehicles to customers, the AR deliveryplatform may conserve resources (e.g., processing resources, memoryresources, transportation resources, real estate resources, and/or like)that would otherwise be used to provide and/or manage the entireinventories of new and/or used vehicles.

FIGS. 1A-1H are diagrams of an example implementation 100 describedherein. As shown in FIGS. 1A-H, a user device, associated with a user(e.g., a sales person associated with a vehicle dealership), may beassociated with an augmented reality (AR) delivery platform. In someimplementations, the user of the user device may be at a locationassociated with a vehicle manufacturer or a vehicle dealership that maynot maintain actual vehicles at the location or in a lot that iswalkable or viewable by the user. In some embodiments, the location mayinclude actual vehicles viewable on site (or associated with the site)as well as a showroom or other area that displays a stock of scalemodels of vehicles. The stock of scale models may be associated with avehicle dealership (as shown in FIG. 1B) or a vehicle manufacturer, andmay represent scale models of an actual make, model, and year (as wellas trim) vehicles that are available or accessible to the vehicledealership or manufacturer. As shown in FIG. 1A, and by reference number105, the AR delivery platform may provide an augmented reality (AR)application to the user device. In some implementations, the user mayutilize the user device to download the AR application from the ARdelivery platform. In some implementations, the AR delivery platform mayautomatically provide the AR application to the user device (e.g., basedon a request from the user device).

In some implementations, the user may utilize the user device to receivethe AR application from the AR delivery platform, and may install the ARapplication on the user device. The user device may be associated withthe location. The AR application may enable the user device to displayoptions for vehicles offered for sale by (or otherwise available to) thedealership (e.g., as represented by the scale models of the vehicle),may associate different AR vehicle information (e.g., vehicle colors,vehicle options, vehicle additions, vehicle interiors) with a capturedimage and/or a streaming image of a scale model of the vehicle, and/orthe like. In this way, the AR application may enable a customer of thedealership to view a variety of options associated with a particularmake, model, and/or year of a vehicle via AR vehicle information and thescale model of the vehicle. Although implementations described hereinrelate to AR, the implementations may also be replicated using virtualreality (VR) or a combination of AR and VR.

In some implementations, a sales person may register the AR applicationand/or the sales person with the AR delivery platform. In suchimplementations, the AR delivery platform may receive, from the userdevice, registration information for registering the AR applicationand/or the sales person with the AR delivery platform. In someimplementations, the registration information may include informationindicating proof of an identity of the sales person (e.g., a name of thesales person, a location of the vehicle dealership, an email address ofthe sales person, and/or the like); information indicating the locationof the vehicle dealership (e.g., global positioning system (GPS)coordinates of the user device, an address of the vehicle dealership,and/or the like); and/or the like.

As shown in FIG. 1B, another user (e.g., a customer or a potentialcustomer of the vehicle dealership) may visit the vehicle dealership andmay utilize the user device with the AR application to review aninventory of vehicles associated with the vehicle dealership. In someimplementations, the vehicle dealership may be replaced with a vehicleshowroom, a pop-up kiosk, a portable display, any location sized todisplay a scale model, a vehicle manufacturer, and/or the like. In someimplementations, the inventory of vehicles may be represented by scalemodels of the vehicles provided at the vehicle dealership. In someimplementations, the scale models of the vehicles may be scaled (e.g.,1:16, 1:24, and/or the like) replicas of the vehicles (e.g., base modelsof the vehicles) that may be manipulated by the AR delivery platform(e.g., via AR vehicle information) to present the inventory of vehicles.In this way, the vehicle dealership may present the entire inventory ofvehicles in a small amount of space (e.g., in a mall, a small store, ata kiosk, and/or the like), thereby reducing costs associated with realestate leases and/or ownership, managing and providing security for theinventory of vehicles, and/or the like. In some implementations, theinventory of actual vehicles may be stored at a location (e.g., withreduced real estate costs) that is close enough to a location of avehicle dealership so that customers may test drive the actual vehicles,or so that an actual vehicle may be provided on-demand (e.g., deliveredto a user location) same day or sooner (e.g., within an hour or hours).

As further shown in FIG. 1B, and by reference number 110, the customermay utilize the user device to capture an image of a scale model of avehicle from the multiple scale models of vehicles, and the user device(e.g., the AR application) may present the image of the scale model ofthe vehicle to the customer (e.g., via a user interface). In someimplementations, the image may include image data of the scale model ofthe vehicle. In some implementations, the customer may utilize the userdevice to capture and display live streaming image data of the scalemodel of the vehicle (e.g., via an electronic viewfinder, of a camera ofthe user device, that electronically projects an image captured by thecamera onto a display of the user device).

As shown in FIG. 1C, and by reference number 115, the AR deliveryplatform may receive, from the user device, the captured image of thescale model of the vehicle. In some implementations, the user device mayprovide the captured image of the scale model of the vehicle to the ARdelivery platform when the image is captured, based on input from thecustomer, based on a request from the AR delivery platform, and/or thelike. In some implementations, the user device may stream the livestreaming image data of the scale model of the vehicle to the ARdelivery platform as the live streaming image data is received by theuser.

As further shown in FIG. 1C, the AR delivery platform may perform imageprocessing and/or machine learning on the captured image to identify avehicle type (e.g., a vehicle make, model, year, and/or the like)associated with the scale model of the vehicle. In some implementations,the user device may perform the image processing and/or the machinelearning on the captured image. In some implementations, the AR deliveryplatform may utilize image analysis, as an image processing technique,to extract meaningful information (e.g., a shape of the scale model ofthe vehicle, text or logos indicating a make and/or a model of the scalemodel of the vehicle, and/or the like) from the captured image. In someimplementations, the image processing technique may includetwo-dimensional (2D) object recognition, three-dimensional (3D) objectrecognition, image segmentation, motion detection, video tracking,machine learning techniques (e.g., Viola-Jones object detectionframework based on Haar features, scale-invariant feature transform(SIFT) (e.g., a feature extraction technique), histogram of orientedgradients (HOG) features (e.g., a feature extraction technique), asupport vector machine, a logistic regression, and/or the like), deeplearning techniques (e.g., region proposals, single shot multiboxdetector (SSD) (e.g., a convolutional neural network or CNN), you onlylook once (YOLO) (e.g., a CNN), and/or the like), and/or the like.

In some implementations, the image processing technique may include acomputer vision technique that receives data from images and/or videosand extracts useful information from the data. The computer visiontechnique may perform image recognition (e.g., determining what isdepicted in an image and/or a video), object detection or objectrecognition (e.g., draw a box around objects in an image and/or avideo), image segmentation (e.g., label each pixel as being part of anobject, draw an outline around an object, etc.), and/or the like. Insome implementations, the computer vision technique may includeperforming feature extraction (e.g., converting pixel data into a moreuseful representation for a model), and providing the extracted featuresto the model to generate predictions. The model may include a supervisedmachine learning model (e.g., a model that progressively improvesperformance on a task by training on labeled data), hand-coded rules,and/or the like. Alternatively, the computer vision technique may omitthe feature extraction and may utilize deep learning approaches togenerate predictions directly from the pixel data. Further details ofthe computer vision technique are provided in U.S. patent applicationSer. No. 15/915,329, filed Mar. 8, 2018, U.S. patent application Ser.No. 15/916,032, filed Mar. 8, 2018, U.S. patent application Ser. No.15/916,137, filed Mar. 8, 2018, the contents of which are incorporatedby reference herein in their entireties.

In some implementations, the scale model of the vehicle may include avehicle type identification mechanism (e.g., an indicia, a barcode, aserial number, a matrix code (e.g., a QR code), and/or the like, whichmay be visible to the human eye or undetectable to the human eye anddetectable using non-visible or infrared light) that may be captured bythe user device and may provide an indication of the vehicle typeassociated with the scale model of the vehicle. In some implementations,the scale model of the vehicle may include AR markers that indicatewhere to provide AR content in relation to the scale model of thevehicle. While the example implementations describe identifying avehicle type of a scale model of a vehicle using image recognition, thetype of the vehicle may be identified by other means, such as scanningor capturing an identification mechanism associated with the model.

In some implementations, the AR delivery platform may process thecaptured image of the scale model of the vehicle, with a machinelearning model, to identify the vehicle type associated with the scalemodel of the vehicle. In some implementations, the machine learningmodel may include a pattern recognition model that identifies thevehicle type associated with the scale model of the vehicle. Forexample, the machine learning model may analyze the captured image ofthe scale model of the vehicle to extract meaningful information (e.g.,a shape of the scale model of the vehicle, text or logos indicating amake and/or a model of the scale model of the vehicle, and/or the like)from the captured image, or may receive the extracted information as aresult of the image processing technique. The machine learning model maycompare the extracted information with information indicating vehicletypes associated with a variety of vehicles, and may match the extractedinformation with at least one of the vehicle types associated with thevariety of vehicles.

In some implementations, the AR delivery platform may perform a trainingoperation on the machine learning model with vehicle type informationassociated with a variety of vehicles. In some implementations, thevehicle type information may include information indicating vehicleshapes, distinguishing vehicle features, logos provided on vehicles,text provided on vehicles, and/or the like.

The AR delivery platform may separate the vehicle type information intoa training set, a validation set, a test set, and/or the like. Thetraining set for image recognition may include sets of labeled images(e.g., thousands of real images or photographs taken at different anglesof the scale model of the vehicle and labeled with the vehicle make,model, and year). In some implementations, the AR delivery platform maytrain the machine learning model using, for example, a supervisedtraining procedure and based on the vehicle type information. Forexample, the AR delivery platform may perform dimensionality reductionto reduce the vehicle type information to a minimum feature set, therebyreducing resources (e.g., processing resources, memory resources, and/orthe like) to train the machine learning model, and may apply aclassification technique to the minimum feature set.

In some implementations, the AR delivery platform may use a logisticregression classification technique to determine a categorical outcome(e.g., that the vehicle type information indicates that a particularvehicle type is associated with a particular shape, particular features,and/or the like). Additionally, or alternatively, the AR deliveryplatform may use a naïve Bayesian classifier technique. In this case,the AR delivery platform may perform binary recursive partitioning tosplit the vehicle type information into partitions and/or branches, anduse the partitions and/or branches to perform predictions (e.g., thatthe vehicle type information indicates that a particular vehicle type isassociated with a particular shape, particular features, and/or thelike). Based on using recursive partitioning, the AR delivery platformmay reduce utilization of computing resources relative to manual, linearsorting and analysis of data points, thereby enabling use of thousands,millions, or billions of data points to train the machine learningmodel, which may result in a more accurate model than using fewer datapoints.

Additionally, or alternatively, the AR delivery platform may use asupport vector machine (SVM) classifier technique and/or logisticregression to generate a non-linear boundary between data points in thetraining set. In this case, the non-linear boundary is used to classifytest data into a particular class.

Additionally, or alternatively, the AR delivery platform may train themachine learning model using a supervised training procedure thatincludes receiving input to the machine learning model from a subjectmatter expert, which may reduce an amount of time, an amount ofprocessing resources, and/or the like to train the machine learningmodel of activity automatability relative to an unsupervised trainingprocedure. In some implementations, the AR delivery platform may use oneor more other model training techniques, such as a neural networktechnique, a latent semantic indexing technique, and/or the like. Forexample, the AR delivery platform may perform an artificial neuralnetwork processing technique (e.g., using a two-layer feedforward neuralnetwork architecture, a three-layer feedforward neural networkarchitecture, and/or the like) to perform pattern recognition withregard to patterns of the vehicle type information. In this case, usingthe artificial neural network processing technique may improve anaccuracy of the trained machine learning model generated by the ARdelivery platform by being more robust to noisy, imprecise, orincomplete data, and by enabling the AR delivery platform to detectpatterns and/or trends undetectable to human analysts or systems usingless complex techniques.

As further shown in FIG. 1C, the AR delivery platform may determinevehicle information, financial information, and/or test driveinformation based on the identified vehicle type associated with thescale model of the vehicle. In some implementations, the vehicleinformation may include information identifying a make of the vehicleassociated with the vehicle type, a model of the vehicle associated withthe vehicle type, a year of the vehicle associated with the vehicletype, colors available for the vehicle associated with the vehicle type,interiors available for the vehicle associated with the vehicle type,colors of the interiors, safety options available for the vehicleassociated with the vehicle type, other vehicle options available forthe vehicle associated with the vehicle type, and/or the like. In someimplementations, the vehicle information may include augmented reality(AR) vehicle information that may be used to associate the vehicleinformation with the image of the scale model of the vehicle and/or withthe scale model of the vehicle. For example, the vehicle informationassociated with the colors available for the vehicle may include imagesand/or text identifying the available colors. When an image and/or textidentifying a particular color is selected and/or hovered over, the ARdelivery platform and/or user device may cause AR vehicle information(e.g., the particular color) to be associated with and displayed on theimage of the scale model of the vehicle and/or on the scale model of thevehicle.

In some implementations, the financial information may be associatedwith actual new or used vehicles and may include a price of an actualvehicle associated with a dealer, a MSRP, and/or the like. In someimplementations, the vehicle options may include colors available on thevehicle lot, colors available from manufacturers, and/or the like. Forexample, based on the scale model captured, vehicle information specificto a particular vehicle on the lot represented by that scale model(e.g., if there are more than one vehicle) may be provided, butadditional vehicle information associated with the multiple vehicles mayalso be provided (e.g., and the customer may select a test drive for anyof the multiple vehicles, obtain financing information specific to oneof the multiple vehicles etc.).

In some implementations, the AR delivery platform may utilize thevehicle information (e.g., the make, model, and/or year) to access adatabase or other backend device to retrieve records associated withreal vehicles, manufacturer configuration information associated withthe vehicles, and/or the like. The database may be specific to adealership, may be limited by location (e.g., within a quantity ofmiles), and/or the like.

In some implementations, the AR delivery platform may utilize thevehicle information to access a database or other backend device toretrieve financial records associated with real vehicles and based on afinancial history of the customer.

In some implementations, the financial information may includeinformation identifying a price for the vehicle associated with thevehicle type, a financing rate for the vehicle associated with thevehicle type, a financing term for the vehicle associated with thevehicle type, financing terms for purchasing the vehicle associated withthe vehicle type, financing terms for leasing the vehicle associatedwith the vehicle type, and/or the like. In some implementations, thefinancial information may be personalized for an actual vehicle andbased on the customer's qualifications (e.g. credit score).

In some implementations, the test drive information may includeinformation identifying whether a test drive is available for thevehicle associated with the vehicle type, a date when the test drive isavailable for the vehicle associated with the vehicle type, a time whenthe test drive is available for the vehicle associated with the vehicletype, a location of the test drive for the vehicle associated with thevehicle type (e.g., the location of the customer, a location remote fromthe location of the customer, and/or the like), and/or the like.

As further shown in FIG. 1C, and by reference number 120, the ARdelivery platform may provide the vehicle information, the financialinformation, and/or the test drive information, to the user device. Theuser device may receive the vehicle information, the financialinformation, and/or the test drive information, and may display thevehicle information, the financial information, and/or the test driveinformation to the customer via a user interface. For example, the userinterface may include vehicle information indicating that the vehiclewas manufactured in 2017, that the vehicle make is a Comobile, that thevehicle model is a Tops, and options available for the vehicle. The userinterface may include financial information indicating that a price ofthe vehicle is $20,000, that a financing rate of the vehicle is 2.5%,that a financing term of the vehicle is five years, and/or financialoptions available for the vehicle. The user interface may include testdrive information indicating that a test drive is available for thevehicle, that the test drive is scheduled for May 5, 2018 at 1:00 PM,and test drive options available for the vehicle. In someimplementations, the user interface may display information associatedwith an image of the scale model, information associated with images ofreal vehicles on a lot, information that is overlaid onto the image ofthe scale model, information that is provided in a “window” above thescale model, and/or the like.

As shown in FIG. 1D, the customer may interact with the vehicleinformation provided by the user interface, and may provide an inputindicating that the customer wishes to, for example, display a pinstripeand a spoiler on the vehicle. Based on the customer input, the userdevice may provide the customer input associated with the vehicleinformation to the AR delivery platform. As shown in FIG. 1D, and byreference number 125, the AR delivery platform may receive the customerinput associated with the vehicle information (e.g., indicating that thecustomer wishes to see a pinstripe and a spoiler on the vehicle).

As further shown in FIG. 1D, the AR delivery platform may determine ARvehicle information, updated financial information, and/or updated testdrive information based on the customer input associated with thevehicle information. In some implementations, the AR vehicle informationmay include vehicle information requested by the customer based on thecustomer input. For example, the AR vehicle information may include ARinformation depicting the pinstripe and the spoiler to be displayed onthe captured image of the scale model of the vehicle. The updatedfinancial information may include the financial information describedabove in connection with FIG. 1C, but may be updated to indicate thatthe price of the vehicle will increase from $20,000 to $22,000 with theaddition of the pinstripe and the spoiler. The test drive informationmay include the test drive information described above in connectionwith FIG. 1C, but may be updated to indicate that no test drive isavailable for a vehicle that includes the pinstripe and the spoiler. Insome implementations, the pinstripe and the spoiler may be applied in ARto the scale model of the vehicle, to a two-dimensional image of thevehicle that is being viewed in AR, to a two-dimensional image of thevehicle being viewed on the display of the user device.

As further shown in FIG. 1D, and by reference number 130, the ARdelivery platform may provide the AR vehicle information, the updatedfinancial information, and/or the updated test drive information to theuser device. The user device may receive the AR vehicle information, theupdated financial information, and/or the updated test driveinformation, and may display the AR vehicle information, the updatedfinancial information, and/or the updated test drive information to thecustomer via a user interface. For example, the user interface mayinclude the vehicle information described above in connection with FIG.1C, and may display an augmented reality pinstripe and spoiler on thecaptured image of the scale model of the vehicle. The user interface mayinclude the financial information described above in connection withFIG. 1C, and information indicating that an updated price of the vehicle(e.g., with the pinstripe and the spoiler) is $22,000. The userinterface may include the test drive information described above inconnection with FIG. 1C, and information indicating that a test drive isnot available for the vehicle with the pinstripe and the spoiler.

As shown in FIG. 1E, the customer may interact with the vehicleinformation provided by the user interface of FIG. 1C, and may providean input indicating that the customer wishes to display the pinstripeand the spoiler on the vehicle. Based on the customer input, the userdevice may provide the customer input associated with the vehicleinformation to the AR delivery platform. As shown in FIG. 1E, and byreference number 135, the AR delivery platform may receive the customerinput associated with the vehicle information (e.g., indicating that thecustomer wishes to see the pinstripe and the spoiler on the vehicle).

As further shown in FIG. 1E, the AR delivery platform may determineprojector information, updated financial information, and/or updatedtest drive information based on the customer input associated with thevehicle information. In some implementations, the projector informationmay include vehicle information requested by the customer based on thecustomer input (e.g., and to be projected on the scale model of thevehicle). For example, the projector information may include informationdepicting the pinstripe and the spoiler (e.g., a holographic rendering)to be projected (e.g., by a projector) on the scale model of thevehicle. The updated financial information may include the financialinformation described above in connection with FIG. 1C, but may beupdated to indicate that the price of the vehicle will increase from$20,000 to $22,000 with the addition of the pinstripe and the spoiler.The test drive information may include the test drive informationdescribed above in connection with FIG. 1C, but may be updated toindicate that no test drive is available for a vehicle that includes thepinstripe and the spoiler.

As further shown in FIG. 1E, and by reference number 140, the ARdelivery platform may provide the updated financial information and/orthe updated test drive information to the user device. The user devicemay receive the updated financial information and/or the updated testdrive information, and may display the updated financial informationand/or the updated test drive information to the customer via a userinterface. For example, the user interface may include the financialinformation described above in connection with FIG. 1C, and informationindicating that an updated price of the vehicle (e.g., with thepinstripe and the spoiler) is $22,000. The user interface may includethe test drive information described above in connection with FIG. 1C,and information indicating that a test drive is not available for thevehicle with the pinstripe and the spoiler.

As further shown in FIG. 1E, and by reference number 145, the ARdelivery platform may provide the projector information to a projectorassociated with the scale model of the vehicle. The projector mayreceive the projector information, and may project the projectorinformation on the scale model of the vehicle. For example, theprojector may project a pinstripe and a spoiler on the scale model ofthe vehicle. In some implementations, the user device may capture animage or streaming image data of the scale model of the vehicle with thepinstripe and the spoiler, and may display the captured image orstreaming image data to the customer.

As shown in FIG. 1F, the customer may interact with the financialinformation provided by the user interface of FIG. 1C, and may providean input indicating that the customer wishes to select a differentfinancing rate (e.g., 1.5%) and a different financing term (e.g., fouryears) from a different financial institution. Based on the customerinput, the user device may provide the customer input associated withthe financial information to the AR delivery platform. As shown in FIG.1F, and by reference number 150, the AR delivery platform may receivethe customer input associated with the financial information (e.g.,indicating that the customer wishes to select a different financing rateand a different financing term from a different financial institution).

As further shown in FIG. 1F, the AR delivery platform may determineupdated financial information based on the customer input associatedwith the financial information. In some implementations, the updatedfinancial information may include the financial information describedabove in connection with FIG. 1C, but may be updated to indicate thedifferent financing rate, the different financing term, and/or adifferent monthly payment for the vehicle based on the differentfinancing rate and/or the different financing term.

As further shown in FIG. 1F, and by reference number 155, the ARdelivery platform may provide the updated financial information to theuser device. The user device may receive the updated financialinformation and may display the updated financial information to thecustomer via a user interface. For example, the user interface mayinclude the financial information described above in connection withFIG. 1C, and information indicating the different financing rate, thedifferent financing term, the different monthly payment for the vehicle,and/or the like.

In some implementations, if the customer wishes to purchase or lease thevehicle based on the financial information or the updated financialinformation, the AR delivery platform may automatically provide, to adevice associated with a financial institution, the financialinformation or the updated financial information, the vehicleinformation, personal information associated with the customer (e.g., aname of the customer, an address of the customer, a credit report of thecustomer, and/or the like, with the permission of the customer), and/orthe like.

As shown in FIG. 1G, the customer may interact with the test driveinformation provided by the user interface of FIG. 1C, and may providean input indicating that the customer wishes to test drive the vehicleon May 5, 2018 at 3:30 PM and wishes to pick up the vehicle in a parkinglot near the customer. Based on the customer input, the user device mayprovide the customer input associated with the test drive information tothe AR delivery platform. As shown in FIG. 1G, and by reference number160, the AR delivery platform may receive the customer input associatedwith the test drive information (e.g., indicating that the customerwishes to test drive the vehicle on May 5, 2018 at 3:30 PM and wishes topick up the vehicle in a parking lot near the customer).

As further shown in FIG. 1G, the AR delivery platform may determineupdated test drive information based on the customer input associatedwith the test drive information. In some implementations, the updatedtest drive information may include the test drive information describedabove in connection with FIG. 1C, but may be updated to indicate theparticular date, time, and location of the test drive (e.g., May 5,2018, 3:30 PM, and the parking lot near the customer).

As further shown in FIG. 1G, and by reference number 165, the ARdelivery platform may provide the updated test drive information to theuser device. The user device may receive the updated test driveinformation and may display the updated test drive information to thecustomer via a user interface. For example, the user interface mayinclude the test drive information described above in connection withFIG. 1C, and information indicating the particular date, time, andlocation of the test drive (e.g., May 5, 2018, 3:30 PM, and the parkinglot near the customer).

In some implementations, the AR delivery platform may automaticallyprovide, to the vehicle, instructions to drive to a particular locationof the user device (e.g., the parking lot near the customer) on theparticular date (e.g., May 5, 2018) and at the particular time (e.g.,3:30 PM). In some implementations, the AR delivery platform may provide,to the user device, walking or driving directions to a location of thevehicle, and the customer may utilize the directions to walk or drive tothe vehicle for the test drive. In some implementations, the AR deliveryplatform may automatically provide, to another user device associatedwith an employee of the vehicle dealership, instructions to drive thevehicle to a particular location of the user device (e.g., the parkinglot near the customer) on the particular date (e.g., May 5, 2018) and atthe particular time (e.g., 3:30 PM). In some implementations, the ARdelivery platform may provide, to the vehicle, navigation instructionsfor the vehicle to autonomously drive to the particular location of theuser device.

As shown in FIG. 1H, and by reference number 170, the actual vehicle maybe provided to the location of the customer (e.g., the parking lot nearthe customer), on the particular date and at the particular time, sothat the customer may test drive the actual vehicle. In someimplementations, the vehicle may automatically drive to the location ofthe customer based on the instructions, provided to the vehicle, todrive to a particular location of the user device (e.g., the parking lotnear the customer) on the particular date (e.g., May 5, 2018) and at theparticular time (e.g., 3:30 PM). In some implementations, the customermay walk or drive to the location of the vehicle based on the walking ordriving directions provided to the user device. In some implementations,the employee of the vehicle dealership may drive the vehicle to thelocation of the customer based on the instructions to drive the vehicleto a particular location of the user device (e.g., the parking lot nearthe customer) on the particular date (e.g., May 5, 2018) and at theparticular time (e.g., 3:30 PM).

In this way, several different stages of the process for utilizingmachine learning to generate augmented reality vehicle information for ascale model of a vehicle are automated, thereby removing humansubjectivity and waste from those stages of the process, and improvingspeed and efficiency of the process and conserving computing resources(e.g., processing resources, memory resources, and/or the like).Furthermore, implementations described herein use a rigorous,computerized process to perform tasks or roles that were not previouslyperformed or were previously performed using subjective human intuitionor input. For example, currently there does not exist a technique thatutilizes machine learning to generate augmented reality vehicleinformation for a scale model of a vehicle. Finally, automating theprocess for utilizing machine learning to generate augmented realityvehicle information for a scale model of a vehicle conserves computingresources (e.g., processing resources, memory resources, and/or thelike) that would otherwise be used to provide and/or manage the entireinventories of new and/or used vehicles.

In some implementations, the image recognition performed by the ARdelivery platform may be configured to identify real vehicles and/orscale models of vehicles. Thus, the AR delivery platform may translate ascale model environment to the real world and may provide real worldvehicle information (e.g., for actual vehicles on a lot, in a database,a generic vehicle that can be configured to purchase) using the scalemodels as proxies for real vehicles. The AR delivery platform mayprocess information differently based on determining whether a vehicleis a scale model or a real vehicle.

As indicated above, FIGS. 1A-1H are provided merely as examples. Otherexamples may differ from what was described with regard to FIGS. 1A-1H.

FIG. 2 is a diagram of an example environment 200 in which systemsand/or methods, described herein, may be implemented. As shown in FIG. 2, environment 200 may include a user device 210, an AR delivery platform220, and a network 230. Devices of environment 200 may interconnect viawired connections, wireless connections, or a combination of wired andwireless connections.

User device 210 includes one or more devices capable of receiving,generating, storing, processing, and/or providing information, such asinformation described herein. For example, user device 210 may include amobile phone (e.g., a smart phone, a radiotelephone, etc.), a laptopcomputer, a tablet computer, a desktop computer, a handheld computer, agaming device, a wearable communication device (e.g., a smartwristwatch, a pair of smart eyeglasses, etc.), or a similar type ofdevice. In some implementations, user device 210 may receive informationfrom and/or transmit information to AR delivery platform 220.

AR delivery platform 220 includes one or more devices that may utilizemachine learning to generate augmented reality vehicle information for ascale model of a vehicle. In some implementations, AR delivery platform220 may be modular such that certain software components may be swappedin or out depending on a particular need. As such, AR delivery platform220 may be easily and/or quickly reconfigured for different uses. Insome implementations, AR delivery platform 220 may receive informationfrom and/or transmit information to one or more user devices 210.

In some implementations, as shown, AR delivery platform 220 may behosted in a cloud computing environment 222. Notably, whileimplementations described herein describe AR delivery platform 220 asbeing hosted in cloud computing environment 222, in someimplementations, AR delivery platform 220 may be non-cloud-based (i.e.,may be implemented outside of a cloud computing environment) or may bepartially cloud-based.

Cloud computing environment 222 includes an environment that may host ARdelivery platform 220. Cloud computing environment 222 may providecomputation, software, data access, storage, etc. services that do notrequire end-user knowledge of a physical location and configuration ofsystem(s) and/or device(s) that host AR delivery platform 220. As shown,cloud computing environment 222 may include a group of computingresources 224 (referred to collectively as “computing resources 224” andindividually as “computing resource 224”).

Computing resource 224 includes one or more personal computers,workstation computers, server devices, or other types of computationand/or communication devices. In some implementations, computingresource 224 may host AR delivery platform 220. The cloud resources mayinclude compute instances executing in computing resource 224, storagedevices provided in computing resource 224, data transfer devicesprovided by computing resource 224, etc. In some implementations,computing resource 224 may communicate with other computing resources224 via wired connections, wireless connections, or a combination ofwired and wireless connections.

As further shown in FIG. 2 , computing resource 224 includes a group ofcloud resources, such as one or more applications (“APPs”) 224-1, one ormore virtual machines (“VMs”) 224-2, virtualized storage (“VSs”) 224-3,one or more hypervisors (“HYPs”) 224-4, and/or the like.

Application 224-1 includes one or more software applications that may beprovided to or accessed by user device 210. Application 224-1 mayeliminate a need to install and execute the software applications onuser device 210. For example, application 224-1 may include softwareassociated with AR delivery platform 220 and/or any other softwarecapable of being provided via cloud computing environment 222. In someimplementations, one application 224-1 may send/receive informationto/from one or more other applications 224-1, via virtual machine 224-2.

Virtual machine 224-2 includes a software implementation of a machine(e.g., a computer) that executes programs like a physical machine.Virtual machine 224-2 may be either a system virtual machine or aprocess virtual machine, depending upon use and degree of correspondenceto any real machine by virtual machine 224-2. A system virtual machinemay provide a complete system platform that supports execution of acomplete operating system (“OS”). A process virtual machine may executea single program, and may support a single process. In someimplementations, virtual machine 224-2 may execute on behalf of a user(e.g., a user of user device 210 or an operator of AR delivery platform220), and may manage infrastructure of cloud computing environment 222,such as data management, synchronization, or long-duration datatransfers.

Virtualized storage 224-3 includes one or more storage systems and/orone or more devices that use virtualization techniques within thestorage systems or devices of computing resource 224. In someimplementations, within the context of a storage system, types ofvirtualizations may include block virtualization and filevirtualization. Block virtualization may refer to abstraction (orseparation) of logical storage from physical storage so that the storagesystem may be accessed without regard to physical storage orheterogeneous structure. The separation may provide administrators ofthe storage system with flexibility in how the administrators managestorage for end users. File virtualization may eliminate dependenciesbetween data accessed at a file level and a location where files arephysically stored. This may enable optimization of storage use, serverconsolidation, and/or performance of non-disruptive file migrations.

Hypervisor 224-4 may provide hardware virtualization techniques thatallow multiple operating systems (e.g., “guest operating systems”) toexecute concurrently on a host computer, such as computing resource 224.Hypervisor 224-4 may present a virtual operating platform to the guestoperating systems, and may manage the execution of the guest operatingsystems. Multiple instances of a variety of operating systems may sharevirtualized hardware resources.

Network 230 includes one or more wired and/or wireless networks. Forexample, network 230 may include a cellular network (e.g., a fifthgeneration (5G) network, a long-term evolution (LTE) network, a thirdgeneration (3G) network, a code division multiple access (CDMA) network,etc.), a public land mobile network (PLMN), a local area network (LAN),a wide area network (WAN), a metropolitan area network (MAN), atelephone network (e.g., the Public Switched Telephone Network (PSTN)),a private network, an ad hoc network, an intranet, the Internet, a fiberoptic-based network, and/or the like, and/or a combination of these orother types of networks.

The number and arrangement of devices and networks shown in FIG. 2 areprovided as an example. In practice, there may be additional devicesand/or networks, fewer devices and/or networks, different devices and/ornetworks, or differently arranged devices and/or networks than thoseshown in FIG. 2 . Furthermore, two or more devices shown in FIG. 2 maybe implemented within a single device and/or a single device shown inFIG. 2 may be implemented as multiple, distributed devices.Additionally, or alternatively, a set of devices (e.g., one or moredevices) of environment 200 may perform one or more functions describedas being performed by another set of devices of environment 200.

FIG. 3 is a diagram of example components of a device 300. Device 300may correspond to user device 210, AR delivery platform 220, and/orcomputing resource 224. In some implementations, user device 210, ARdelivery platform 220, and/or computing resource 224 may include one ormore devices 300 and/or one or more components of device 300. As shownin FIG. 3 , device 300 may include a bus 310, a processor 320, a memory330, a storage component 340, an input component 350, an outputcomponent 360, and/or a communication interface 370.

Bus 310 includes a component that permits communication among thecomponents of device 300. Processor 320 is implemented in hardware,firmware, or a combination of hardware and software. Processor 320 is acentral processing unit (CPU), a graphics processing unit (GPU), anaccelerated processing unit (APU), a microprocessor, a microcontroller,a digital signal processor (DSP), a field-programmable gate array(FPGA), an application-specific integrated circuit (ASIC), or anothertype of processing component. In some implementations, processor 320includes one or more processors capable of being programmed to perform afunction. Memory 330 includes a random-access memory (RAM), a read onlymemory (ROM), and/or another type of dynamic or static storage device(e.g., a flash memory, a magnetic memory, and/or an optical memory) thatstores information and/or instructions for use by processor 320.

Storage component 340 stores information and/or software related to theoperation and use of device 300. For example, storage component 340 mayinclude a hard disk (e.g., a magnetic disk, an optical disk, amagneto-optic disk, and/or a solid-state disk), a compact disc (CD), adigital versatile disc (DVD), a floppy disk, a cartridge, a magnetictape, and/or another type of non-transitory computer-readable medium,along with a corresponding drive.

Input component 350 includes a component that permits device 300 toreceive information, such as via user input (e.g., a touch screendisplay, a keyboard, a keypad, a mouse, a button, a switch, and/or amicrophone). Additionally, or alternatively, input component 350 mayinclude a sensor for sensing information (e.g., a global positioningsystem (GPS) component, an accelerometer, a gyroscope, and/or anactuator). Output component 360 includes a component that providesoutput information from device 300 (e.g., a display, a speaker, and/orone or more light-emitting diodes (LEDs)).

Communication interface 370 includes a transceiver-like component (e.g.,a transceiver and/or a separate receiver and transmitter) that enablesdevice 300 to communicate with other devices, such as via a wiredconnection, a wireless connection, or a combination of wired andwireless connections. Communication interface 370 may permit device 300to receive information from another device and/or provide information toanother device. For example, communication interface 370 may include anEthernet interface, an optical interface, a coaxial interface, aninfrared interface, a radio frequency (RF) interface, a universal serialbus (USB) interface, a Wi-Fi interface, a cellular network interface,and/or the like.

Device 300 may perform one or more processes described herein. Device300 may perform these processes based on processor 320 executingsoftware instructions stored by a non-transitory computer-readablemedium, such as memory 330 and/or storage component 340. Acomputer-readable medium is defined herein as a non-transitory memorydevice. A memory device includes memory space within a single physicalstorage device or memory space spread across multiple physical storagedevices.

Software instructions may be read into memory 330 and/or storagecomponent 340 from another computer-readable medium or from anotherdevice via communication interface 370. When executed, softwareinstructions stored in memory 330 and/or storage component 340 may causeprocessor 320 to perform one or more processes described herein.Additionally, or alternatively, hardwired circuitry may be used in placeof or in combination with software instructions to perform one or moreprocesses described herein. Thus, implementations described herein arenot limited to any specific combination of hardware circuitry andsoftware.

The number and arrangement of components shown in FIG. 3 are provided asan example. In practice, device 300 may include additional components,fewer components, different components, or differently arrangedcomponents than those shown in FIG. 3 . Additionally, or alternatively,a set of components (e.g., one or more components) of device 300 mayperform one or more functions described as being performed by anotherset of components of device 300.

FIG. 4 is a flow chart of an example process 400 for utilizing machinelearning to generate augmented reality vehicle information for a scalemodel of a vehicle. In some implementations, one or more process blocksof FIG. 4 may be performed by an AR delivery platform (e.g., AR deliveryplatform 220). In some implementations, one or more process blocks ofFIG. 4 may be performed by another device or a group of devices separatefrom or including the AR delivery platform, such as a user device (e.g.,user device 210).

As shown in FIG. 4 , process 400 may include receiving an image of ascale model of a vehicle, wherein the image is transmitted by a userdevice to the device (block 410). For example, the AR delivery platform(e.g., using computing resource 224, processor 320, communicationinterface 370, and/or the like) may receive an image of a scale model ofa vehicle, as described above in connection with FIGS. 1A-2 . In someimplementations, the image may be transmitted by a user device to thedevice.

As further shown in FIG. 4 , process 400 may include processing theimage, with a model, to determine vehicle information associated withthe scale model of the vehicle, wherein the vehicle information includesat least one of the image of the scale model of the vehicle, informationidentifying a year of the vehicle, information identifying a make of thevehicle, or information identifying a model of the vehicle (block 420).For example, the AR delivery platform (e.g., using computing resource224, processor 320, memory 330, and/or the like) may process the image,with a model, to determine vehicle information associated with the scalemodel of the vehicle, as described above in connection with FIGS. 1A-2 .In some implementations, the vehicle information may include at leastone of the image of the scale model of the vehicle, informationidentifying a year of the vehicle, information identifying a make of thevehicle, or information identifying a model of the vehicle.

As further shown in FIG. 4 , process 400 may include providing thevehicle information to the user device (block 430). For example, the ARdelivery platform (e.g., using computing resource 224, processor 320,memory 330, communication interface 370, and/or the like) may providethe vehicle information to the user device, as described above inconnection with FIGS. 1A-2 .

As further shown in FIG. 4 , process 400 may include receiving, from theuser device, an input associated with the vehicle information (block440). For example, the AR delivery platform (e.g., using computingresource 224, processor 320, communication interface 370, and/or thelike) may receive, from the user device, an input associated with thevehicle information, as described above in connection with FIGS. 1A-2 .

As further shown in FIG. 4 , process 400 may include determiningaugmented reality vehicle information based on the input associated withthe vehicle information, wherein the augmented reality vehicleinformation includes augmented reality information to be associated withthe image of the scale model of the vehicle (block 450). For example,the AR delivery platform (e.g., using computing resource 224, processor320, memory 330, and/or the like) may determine augmented realityvehicle information based on the input associated with the vehicleinformation, as described above in connection with FIGS. 1A-2 . In someimplementations, the augmented reality vehicle information may includeaugmented reality information to be associated with the image of thescale model of the vehicle.

As further shown in FIG. 4 , process 400 may include providing theaugmented reality vehicle information to the user device to enable theuser device to associate the augmented reality vehicle information withthe image of the scale model of the vehicle (block 460). For example,the AR delivery platform (e.g., using computing resource 224, processor320, communication interface 370, and/or the like) may provide theaugmented reality vehicle information to the user device to enable theuser device to associate the augmented reality vehicle information withthe image of the scale model of the vehicle, as described above inconnection with FIGS. 1A-2 .

Process 400 may include additional implementations, such as any singleimplementation or any combination of implementations described belowand/or described with regard to any other process described herein.

In some implementations, the AR delivery platform may determine aparticular vehicle in inventory that includes features thatsubstantially match the augmented reality information, may determinefinancial information for the particular vehicle, where the financialinformation includes at least one of information indicating a price ofthe particular vehicle, or information indicating financing terms forthe particular vehicle, and may provide the financial information to theuser device. In some implementations, the AR delivery platform mayreceive, from the user device, an input associated with the financialinformation, may determine updated financial information based on theinput associated with the financial information, and may provide theupdated financial information to the user device.

In some implementations, the AR delivery platform may determine testdrive information based on the vehicle type associated with the scalemodel of the vehicle, where the test drive information includes at leastone of information indicating that a test drive is available for thevehicle, or information indicating a time of the test drive.Additionally, the AR delivery platform may receive, from the userdevice, an input associated with the test drive information, maydetermine updated test drive information based on the input associatedwith the test drive information, where the updated test driveinformation includes information indicating an updated time for the testdrive of the vehicle, may provide, to the vehicle, instructions to driveto a location of the user device at the updated time, and may providethe updated test drive information to the user device.

In some implementations, the AR delivery platform may determine updatedvehicle information based on the input associated with the vehicleinformation, and may provide the updated vehicle information to the userdevice. In some implementations, the AR delivery platform may determineprojector information based on the input associated with the vehicleinformation, where the projector information includes information to beprojected on the scale model of the vehicle, and may provide, to aprojector associated with the scale model of the vehicle, the projectorinformation to enable the projector to project the projector informationon the scale model of the vehicle. In some implementations, the ARdelivery platform may provide, to the user device, an augmented realityapplication to enable the user device to display the augmented realityvehicle information.

Although FIG. 4 shows example blocks of process 400, in someimplementations, process 400 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 4 . Additionally, or alternatively, two or more of theblocks of process 400 may be performed in parallel.

FIG. 5 is a flow chart of an example process 500 for utilizing machinelearning to generate augmented reality vehicle information for a scalemodel of a vehicle. In some implementations, one or more process blocksof FIG. 5 may be performed by an AR delivery platform (e.g., AR deliveryplatform 220). In some implementations, one or more process blocks ofFIG. 5 may be performed by another device or a group of devices separatefrom or including the AR delivery platform, such as a user device (e.g.,user device 210).

As shown in FIG. 5 , process 500 may include receiving an imageincluding image data of a scale model of a vehicle, wherein the image iscaptured by a user device (block 510). For example, the AR deliveryplatform (e.g., using computing resource 224, processor 320,communication interface 370, and/or the like) may receive an imageincluding image data of a scale model of a vehicle, as described abovein connection with FIGS. 1A-2 . In some implementations, the image maybe captured by a user device.

As further shown in FIG. 5 , process 500 may include processing theimage data, with a model, to identify a make, a model, and/or a yearrepresented by the scale model of the vehicle (block 520). For example,the AR delivery platform (e.g., using computing resource 224, processor320, storage component 340, and/or the like) may process the image data,with a model, to identify a make, a model, and/or a year represented bythe scale model of the vehicle, as described above in connection withFIGS. 1A-2 .

As further shown in FIG. 5 , process 500 may include determiningaugmented reality vehicle information of a real world version of thevehicle based on the make, the model, and/or the year represented by thescale model of the vehicle, wherein the augmented reality vehicleinformation includes augmented reality information to be associated withthe image of the scale model of the vehicle (block 530). For example,the AR delivery platform (e.g., using computing resource 224, processor320, memory 330, and/or the like) may determine augmented realityvehicle information of a real-world version of the vehicle based on themake, the model, and/or the year represented by the scale model of thevehicle, as described above in connection with FIGS. 1A-2 . In someimplementations, the augmented reality vehicle information may includeaugmented reality information to be associated with the image of thescale model of the vehicle.

As further shown in FIG. 5 , process 500 may include providing theaugmented reality vehicle information to the user device to enable theuser device to associate the augmented reality vehicle information withthe image of the scale model of the vehicle (block 540). For example,the AR delivery platform (e.g., using computing resource 224, processor320, communication interface 370, and/or the like) may provide theaugmented reality vehicle information to the user device to enable theuser device to associate the augmented reality vehicle information withthe image of the scale model of the vehicle, as described above inconnection with FIGS. 1A-2 .

As further shown in FIG. 5 , process 500 may include receiving, from theuser device, an input associated with the augmented reality vehicleinformation (block 550). For example, the AR delivery platform (e.g.,using computing resource 224, processor 320, communication interface370, and/or the like) may receive, from the user device, an inputassociated with the augmented reality vehicle information, as describedabove in connection with FIGS. 1A-2 .

As further shown in FIG. 5 , process 500 may include determining updatedaugmented reality vehicle information based on the input associated withthe augmented reality vehicle information (block 560). For example, theAR delivery platform (e.g., using computing resource 224, processor 320,memory 330, and/or the like) may determine updated augmented realityvehicle information based on the input associated with the augmentedreality vehicle information, as described above in connection with FIGS.1A-2 .

As further shown in FIG. 5 , process 500 may include providing theupdated augmented reality vehicle information to the user device toenable the user device to associate the updated augmented realityvehicle information with the image of the scale model of the vehicle(block 570). For example, the AR delivery platform (e.g., usingcomputing resource 224, processor 320, communication interface 370,and/or the like) may provide the updated augmented reality vehicleinformation to the user device to enable the user device to associatethe updated augmented reality vehicle information with the image of thescale model of the vehicle, as described above in connection with FIGS.1A-2 .

Process 500 may include additional implementations, such as any singleimplementation or any combination of implementations described belowand/or described with regard to any other process described herein.

In some implementations, the AR delivery platform may determinefinancial information based on the make, the model, and/or the yearrepresented by the scale model of the vehicle, where the financialinformation includes at least one of information indicating a price ofthe vehicle, or information requesting information associated with auser of the user device, and may provide the financial information tothe user device. In some implementations, the AR delivery platform mayreceive, from the user device, the information associated with the userof the user device, may determine updated financial information based onthe information associated with the user of the user device, where theupdated financial information includes information indicating financingterms for the user, and may provide the updated financial information tothe user device.

In some implementations, the AR delivery platform may determine testdrive information for a vehicle that includes features substantiallysimilar to the make, the model, and/or the year represented by the scalemodel of the vehicle (e.g., the includes sixty percent, seventy percent,eighty percent, and/or the like of the features of the scale model ofthe vehicle), where the test drive information includes informationindicating that a test drive is available for the vehicle, may determineaugmented reality test drive information based on the test driveinformation, and may provide, to the user device, the augmented realitytest drive information to enable the user to experience an augmentedreality test drive of the vehicle via the user device. In someimplementations, the augmented reality vehicle information may beassociated with one or more of an optional color of the vehicle, anoptional accessory of the vehicle, or an option associated with thevehicle.

In some implementations, the AR delivery platform may determineprojector information based on the make, the model, and the yearrepresented by the scale model of the vehicle, where the projectorinformation includes information to be projected on the scale model ofthe vehicle, and may provide, to a projector associated with the scalemodel of the vehicle, the projector information to enable the projectorto project the projector information on the scale model of the vehicle.In some implementations, the AR delivery platform may provide, to theuser device, an application to enable the user device to display theaugmented reality vehicle information and the updated augmented realityvehicle information.

Although FIG. 5 shows example blocks of process 500, in someimplementations, process 500 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 5 . Additionally, or alternatively, two or more of theblocks of process 500 may be performed in parallel.

FIG. 6 is a flow chart of an example process 600 for utilizing machinelearning to generate augmented reality vehicle information for a scalemodel of a vehicle. In some implementations, one or more process blocksof FIG. 6 may be performed by a user device (e.g., user device 210). Insome implementations, one or more process blocks of FIG. 6 may beperformed by another device or a group of devices separate from orincluding the user device, such as an AR delivery platform (e.g., ARdelivery platform 220).

As shown in FIG. 6 , process 600 may include receiving an image of ascale model of a vehicle (block 610). For example, the user device(e.g., using computing resource 224, processor 320, input component 350,communication interface 370, and/or the like) may receive an image of ascale model of a vehicle, as described above in connection with FIGS.1A-2 .

As further shown in FIG. 6 , process 600 may include processing theimage, with a model, to identify a vehicle type associated with thescale model of the vehicle (block 620). For example, the user device(e.g., using computing resource 224, processor 320, storage component340, and/or the like) may process the image, with a model, to identify avehicle type associated with the scale model of the vehicle, asdescribed above in connection with FIGS. 1A-2 .

As further shown in FIG. 6 , process 600 may include determiningaugmented reality vehicle information based on the vehicle typeassociated with the scale model of the vehicle, wherein the augmentedreality vehicle information includes augmented reality information to beassociated with the image of the scale model of the vehicle (block 630).For example, the user device (e.g., using computing resource 224,processor 320, memory 330, and/or the like) may determine augmentedreality vehicle information based on the vehicle type associated withthe scale model of the vehicle, as described above in connection withFIGS. 1A-2 . In some implementations, the augmented reality vehicleinformation may include augmented reality information to be associatedwith the image of the scale model of the vehicle.

As further shown in FIG. 6 , process 600 may include providing, fordisplay, the augmented reality vehicle information in association withthe image of the scale model of the vehicle (block 640). For example,the user device (e.g., using computing resource 224, processor 320,output component 360, communication interface 370, and/or the like) mayprovide, for display, the augmented reality vehicle information inassociation with the image of the scale model of the vehicle, asdescribed above in connection with FIGS. 1A-2 .

As further shown in FIG. 6 , process 600 may include receiving an inputassociated with the augmented reality vehicle information (block 650).For example, the user device (e.g., using computing resource 224,processor 320, input component 350, communication interface 370, and/orthe like) may receive an input associated with the augmented realityvehicle information, as described above in connection with FIGS. 1A-2 .

As further shown in FIG. 6 , process 600 may include determining updatedaugmented reality vehicle information based on the input associated withthe augmented reality vehicle information (block 660). For example, theuser device (e.g., using computing resource 224, processor 320, memory330, and/or the like) may determine updated augmented reality vehicleinformation based on the input associated with the augmented realityvehicle information, as described above in connection with FIGS. 1A-2 .

As further shown in FIG. 6 , process 600 may include providing, fordisplay, the updated augmented reality vehicle information inassociation with the image of the scale model of the vehicle (block670). For example, the user device (e.g., using computing resource 224,processor 320, output component 360, communication interface 370, and/orthe like) may provide, for display, the updated augmented realityvehicle information in association with the image of the scale model ofthe vehicle, as described above in connection with FIGS. 1A-2 .

Process 600 may include additional implementations, such as any singleimplementation or any combination of implementations described belowand/or described with regard to any other process described herein.

In some implementations, the user device may provide, to a device, thevehicle type associated with the scale model of the vehicle, and mayreceive, from the device, financial information based on the vehicletype associated with the scale model of the vehicle, where the financialinformation includes at least one of information indicating a price ofthe vehicle, or information indicating financing terms for the vehicle.In some implementations, the user device may receive, from a device, anapplication to enable the user device to display the augmented realityvehicle information and the updated augmented reality vehicleinformation.

In some implementations, the augmented reality vehicle information maybe associated with one or more of a color of the vehicle, an accessoryof the vehicle, or an option associated with the vehicle. In someimplementations, the user device may determine projector informationbased on the vehicle type associated with the scale model of thevehicle, where the projector information includes information to beprojected on the scale model of the vehicle, and may provide, to aprojector associated with the scale model of the vehicle, the projectorinformation to enable the projector to project the projector informationon the scale model of the vehicle.

Although FIG. 6 shows example blocks of process 600, in someimplementations, process 600 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 6 . Additionally, or alternatively, two or more of theblocks of process 600 may be performed in parallel.

The foregoing disclosure provides illustration and description, but isnot intended to be exhaustive or to limit the implementations to theprecise form disclosed. Modifications and variations may be made inlight of the above disclosure or may be acquired from practice of theimplementations.

As used herein, the term component is intended to be broadly construedas hardware, firmware, or a combination of hardware and software.

Certain user interfaces have been described herein and/or shown in thefigures. A user interface may include a graphical user interface, anon-graphical user interface, a text-based user interface, or the like.A user interface may provide information for display. In someimplementations, a user may interact with the information, such as byproviding input via an input component of a device that provides theuser interface for display. In some implementations, a user interfacemay be configurable by a device and/or a user (e.g., a user may changethe size of the user interface, information provided via the userinterface, a position of information provided via the user interface,etc.). Additionally, or alternatively, a user interface may bepre-configured to a standard configuration, a specific configurationbased on a type of device on which the user interface is displayed,and/or a set of configurations based on capabilities and/orspecifications associated with a device on which the user interface isdisplayed.

It will be apparent that systems and/or methods, described herein, maybe implemented in different forms of hardware, firmware, or acombination of hardware and software. The actual specialized controlhardware or software code used to implement these systems and/or methodsis not limiting of the implementations. Thus, the operation and behaviorof the systems and/or methods were described herein without reference tospecific software code—it being understood that software and hardwaremay be designed to implement the systems and/or methods based on thedescription herein.

Even though particular combinations of features are recited in theclaims and/or disclosed in the specification, these combinations are notintended to limit the disclosure of possible implementations. In fact,many of these features may be combined in ways not specifically recitedin the claims and/or disclosed in the specification. Although eachdependent claim listed below may directly depend on only one claim, thedisclosure of possible implementations includes each dependent claim incombination with every other claim in the claim set.

No element, act, or instruction used herein should be construed ascritical or essential unless explicitly described as such. Also, as usedherein, the articles “a” and “an” are intended to include one or moreitems, and may be used interchangeably with “one or more.” Furthermore,as used herein, the term “set” is intended to include one or more items(e.g., related items, unrelated items, a combination of related andunrelated items, etc.), and may be used interchangeably with “one ormore.” Where only one item is intended, the term “only one” or similarlanguage is used. Also, as used herein, the terms “has,” “have,”“having,” or the like are intended to be open-ended terms. Further, thephrase “based on” is intended to mean “based, at least in part, on”unless explicitly stated otherwise.

What is claimed is:
 1. A method, comprising: providing, by a device andto a backend device, information associated with a model, wherein thebackend device is associated with one or more features available forordering, and wherein the model is a scale model of a vehicle;receiving, by the first device, from the backend device, and based onthe information associated with the model, information related to thevehicle; receiving, by the first device and from the backend device, atleast one of augmented reality information or virtual realityinformation, wherein the at least one of the augmented realityinformation or the virtual reality information is associated with theinformation related to the vehicle; and causing, by the first device,the at least one of the augmented reality information or virtual realityinformation to be displayed.
 2. The method of claim 1, wherein thebackend device is related to at least one of a location or a dealership.3. The method of claim 1, wherein the information related to the vehicleincludes financial information associated with a customer.
 4. The methodof claim 1, further comprising: receiving, from another device, inputassociated with at least one of: the information related to the vehicle,the augmented reality information, or the virtual reality information;and causing, based on the input, the at least one of the augmentedreality information or the virtual reality information to be updated andprovided for display.
 5. The method of claim 1, further comprising:receiving image or video data; and processing, based on determiningwhether the image or video data is associated with the model or thevehicle, information associated with the model or the vehicle.
 6. Themethod of claim 1, wherein causing the at least one of the augmentedreality information or the virtual reality information to be displayedcomprises at least one of: providing the at least one of the augmentedreality information or the virtual reality information to the backenddevice, or providing projector information to a projector associatedwith the model.
 7. The method of claim 1, wherein the informationrelated to the vehicle includes at least one option associated with thevehicle.
 8. A device, comprising: one or more memories; and one or moreprocessors, coupled to the one or more memories, configured to: provide,to a backend device, information associated with a scale model of avehicle, wherein the backend device is associated with one or morefeatures available for ordering; receive from the backend device andbased on the information associated with the scale model, informationrelated to the vehicle; receive, from the backend device, at least oneof augmented reality information or virtual reality information, whereinthe at least one of the augmented reality information or the virtualreality information is associated with the information related to thevehicle; and cause the at least one of the augmented reality informationor the virtual reality information to be displayed.
 9. The device ofclaim 8, wherein the backend device is related to at least one of alocation or a dealership.
 10. The device of claim 8, wherein theinformation related to the vehicle includes financial information dataassociated with a customer.
 11. The device of claim 8, wherein the oneor more processors are further configured to: receive, from anotherdevice, input associated with at least one of: the information relatedto the vehicle, the augmented reality information, or the virtualreality information; and cause, based on the input, the at least one ofthe augmented reality information or the virtual reality information tobe updated and provided for display.
 12. The device of claim 8, whereinthe one or more processors are further configured to: receive image orvideo data; and process, based on determining whether the image or videodata is associated with the scale model or the vehicle, informationassociated with the scale model or the vehicle.
 13. The device of claim8, wherein the one or more processors, to cause the at least one of theaugmented reality information or the virtual reality information to bedisplayed, are configured to: provide the at least one of the augmentedreality information or the virtual reality information to the backenddevice, or provide projector information to a projector associated withthe scale model.
 14. The device of claim 8, wherein the informationrelated to the vehicle includes at least one option associated with thevehicle.
 15. A non-transitory computer-readable medium storing a set ofinstructions, the set of instructions comprising: one or moreinstructions that, when executed by one or more processors of a device,cause the device to: provide, to a backend device, informationassociated with a scale model related to a vehicle, wherein the backenddevice is associated with one or more features available for ordering;receive from the backend device and based on the information associatedwith the scale model, information related to the vehicle; receive, fromthe backend device, at least one of augmented reality information orvirtual reality information, wherein the at least one of the augmentedreality information or the virtual reality information is associatedwith the information related to the vehicle; and cause the at least oneof the augmented reality information or the virtual reality informationto be displayed.
 16. The non-transitory computer-readable medium ofclaim 15, wherein the backend device is related to at least one of alocation or a dealership.
 17. The non-transitory computer-readablemedium of claim 15, wherein the information related to the vehicleincludes financial information data associated with a customer.
 18. Thenon-transitory computer-readable medium of claim 15, wherein the one ormore instructions further cause the device to: receive, from anotherdevice, input associated with at least one of: the information relatedto the vehicle, the augmented reality information, or the virtualreality information; and cause, based on the input, the at least one ofthe augmented reality information or the virtual reality information tobe updated and provided for display.
 19. The non-transitorycomputer-readable medium of claim 15, wherein the one or moreinstructions further cause the device to: receive image or video data;and process, based on determining whether the image or video data isassociated with the scale model or the vehicle, information associatedwith the scale model or the vehicle.
 20. The non-transitorycomputer-readable medium of claim 15, wherein the one or moreinstructions, that cause the device to cause the at least one of theaugmented reality information or the virtual reality information to bedisplayed, cause the device to: provide the at least one of theaugmented reality information or the virtual reality information to thebackend device, or provide projector information to a projectorassociated with the scale model.